Fraud Detection Tool Predicts Hospital Quality

Researchers at Thomas Jefferson University (Philadelphia, PA, USA) developed a theoretical model to justify the application of PRIDIT—a method originally developed for the detection of health-care fraud—in the investigation of hospital quality. They then applied the PRIDIT method to a national, multiyear data set on US hospital quality variables and outcomes, creating a steady, predictable scale for hospital quality so that actuaries could map out reimbursement rates over years for programs like Medicare and the Patient Protection Affordable Care Act (ACTA).

The results demonstrate how the PRIDIT method can be used to predict future health outcomes based on currently available quality measures. The method scoring of hospital quality includes indicators such as patient satisfaction and medical outcomes, and is weighted heavily by factors such as mortality rates and the number of beds at the hospital, as more beds indicates more cases and better outcomes. Under the model, a patient in a highly rated hospital might dislike the noise and bad food, but survive a life-threatening heart attack. The study was published online on September 30, 2013, in Risk Management and Insurance Review.

“There is a lot of information patients can use to select a hospital. However, this is usually a laundry list of indicators that may not mean much for the lay person, or that they may be unaware even exists,” said lead author Assistant Professor Robert D. Lieberthal, PhD, of the school of population health. “Our method compares hospitals directly, so that a patient choosing between two or three hospitals can easily compare them and choose the highest quality facility.”

The PRIDIT method makes use of fraud predictor variables to obtain an overall suspicion score for each claim, and to classify these claims accordingly without access to a set of audited claims. It is based on a scoring technique called RIDIT (Relative to an Identified Distribution Integral Transform) analysis developed for epidemiological literature.

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